fbpx

Comprehensive Machine Learning

4 out of 5
4
6 reviews

About the Course – With no prior experience in the field of Data Science to being a successful Machine Learning professional. This Course has been designed specifically for those who has no prior experience in Data Science but wants to become a successful Data Scientist.

This course covers everything from Scratch to get you comprehensive understanding and hands on experience on Python and other topics which are needed to be a successful Machine Learning professional. A true hands on experience along with real life case studies and problem statement are included to give you actual project experience.

Please check the Course Curriculum for details of the Topics which will be covered during the course.

Join the Next Batch to learn Online in Live Interactive Class format.

Installation Details

1
• Hardware and Software Requirements
2
• Installation Guide

How to Navigate the Course Documents

1
• Navigation Guidelines for DataCurators LMS

About Community Forum

1
• What is Community Forum
2
• How to register to Community Forum

Python : Let’s build the foundation

1
• An Introduction to Python

Let us Python - Basics

1
• The print statement , Hello World of Sql
2
• Comments
3
• Variables in Python
4
• Operators
5
• String Operations in Python – Slicing
6
• Logical Operations and Operators
7
• Simple Input and Output
8
• Simple Output Formatting
9
• Questions and Quizzes

Data Type in Python

1
• Number
2
• String
3
• Float
4
• List
5
• Tuple
6
• Dictionary
7
• Sets
8
• Frozen set
9
• Questions and Quizzes

Python Program Flow

1
• Indentation
2
• The If statement and its’ related statement
3
• Example with if and else related statement
4
• For loop
5
• Examples for looping
6
• While loop
7
• Range statement
8
• Break
9
• Additional notes
10
• Questions and Quizzes

Functions & Modules

1
• Functions Basics
2
• Default Parameters
3
• Create your own functions
4
• Scope of a Function
5
• Lambda Functions
6
• Map
7
• An Exercise with functions
8
• Questions and Quizzes

Comprehensions in Python

1
• Comprehensions Introduction and Hands on
2
• Questions and Quizzes

Classes In Python

1
• Creating Classes
2
• Instance Methods
3
• Inheritance
4
• Questions and Quizzes

Exception Handling

1
• How to perform Debugging in Python

Introduction to NumPy

1
Comprehensive Numpy Learning
2
• Questions and Quizzes

Data Manipulation with Pandas

1
• Pandas learning – like Pandas size
2
• Introducing to Pandas
3
• Pandas Indexing and Selection
4
• Operating on Pandas Data
5
• Handling Missing Data
6
• Combining Datasets
7
• Aggregation and Grouping
8
• Working with Pivot Tables
9
• Vectorized String Operations
10
• Working with Pandas Time Series
11
• High-Performance Pandas
12
• Further Resources
13
• Questions and Quizzes

Visualization with Matplotlib

1
• Introduction to Python Visualization Libraries
2
• Matplotlib and Seaborn
3
• Questions and Quizzes

Linear Algebra

1
• Level of Detail (LOD)
2
• Bins and Distributions
3
• Window calculations
4
• Data blending and joining
5
• Reference lines
6
• Forecast
7
• Trend line
8
• Clustering
9
• WMS Mapping
10
• Sparse matrices in Machine Learning
11
• Tensors and Tensor Arithmetic
12
• NumPy in Linear Algebra
13
• NumPy N-dimensional Array
14
• Eigen Decomposition with example
15
• Single value Decomposition with example
16
• Linear Algebra and statistics
17
• Principal Component Analysis
18
• Solving a problem using Linear Algebra
19
• Questions and Quizzes

Descriptive Statistics

1
• What is Statistics
2
• Basic Statistics terms
3
• Type of Data
4
• Understanding Variables
5
• Descriptive Vs Inferential Statistics
6
• MMM in everyday terms with formula
7
• Statistics Measures of central tendency
8
• Outliers
9
• Best measure of central tendency based on types of variable
10
• Statistical Dispersion
11
• Questions and Quizzes

Inferential Statistics

1
• Basics of Probability
2
• Statistical Inference and probability theory
3
• Use of Probability in machine Learning
4
• Understanding Basic Probability terms
5
• Types of Events
6
• Types of probability
7
• Rules used in Probability
8
• Bayesian Probability
9
• Understanding Distributions and Expected Values
10
• Understanding Hypothesis Testing
11
• Questions and Quizzes

Exploratory Data Analysis using Python

1
• Data Gathering
2
• Data Cleaning
3
• Data Normalization
4
• Data Transformation
5
• Questions and Quizzes

Supervised Machine Learning – Model Building using Python

1
• Regression Analysis
2
• Gradient Descent
3
• Classification Analysis and Model Building
4
• Pros and Cons of each Algorithm
5
• K-Fold Cross Validation technique
6
• Hyperparameter Tuning using GridSearchCV
7
• Use Case and Quizzes

Model Comparison

1
• How to Measure a Model Performance
2
• How to select a Model for a problem statement
3
• Feature selection methods
4
• Dimension Reduction
5
• Use Case and Quizzes

Model Boosting Technique

Un-Supervised Machine Learning – Model Building using Python

1
• Clustering Analysis
2
K-means Clustering
3
Naïve Bayes Classification
4
Support Vector Machines
5
Logistic Regression
6
Decision Tree
7
Random Forest
8
Principal Component Analysis
9
• Use Cases and Quizzes

Introduction to NLP

Introduction to Deep Learning

Projects on ML

Interview Prepration

1
Building a good resume
2
Mock Interviews

Get Set Go !!

This is Online Live Courses. Classes will be conducted Live and it will be as Interactive as you may find during physical classroom session. You would have all the access to unmute at any given point of time to ask any questions you may have.
We at this moment don't have any EMI options available. You can Pay the fee in Instalments indirectly by taking the help of your credit card service provider to convert the payment into monthly EMI's.

Next Batch Starting 5th Sept , 2020

Time - 5.30 PM - 7.30 PM IST

Days - Saturday and Sunday Classes


Next Batch Starting 3rd Oct , 2020

Time - 8 AM - 10 AM IST

Days - Saturday and Sunday Classes


Add to Wishlist
Enrolled: 99 students
Duration: 5 Months
Lectures: 124
Level: Advanced

Working hours

Monday 10:00 am - 11.30 pm
Tuesday 10:00 am - 11.30 pm
Wednesday 10:00 am - 11.30 pm
Thursday 10:00 am - 11.30 pm
Friday 10:00 am - 11.30 pm
Saturday 9:00 am - 9.00 pm
Sunday 9:00 am - 9.00 pm